Q-learning is a popular model-free reinforcement learning algorithm used in artificial intelligence to help agents learn how to make decisions in uncertain environments. It operates by learning a value function, known as the Q-function, which estimates the expected utility of taking a specific action in a given state. Through a process of exploration and exploitation, the agent updates its Q-values based on the rewards received from its actions, gradually converging towards an optimal policy that maximizes cumulative rewards over time. Q-learning is widely used in various applications, including robotics, game playing, and autonomous systems, as it allows agents to learn effective strategies without needing a model of the environment.